Traditional to Transformers: A Survey on Current Trends and Future Prospects for Hyperspectral Image Classification
Hyperspectral image classification is a challenging task due to the high dimensionality and
complex nature of hyperspectral data. In recent years, deep learning techniques have …
complex nature of hyperspectral data. In recent years, deep learning techniques have …
Dual-TranSpeckle: Dual-pathway transformer based encoder-decoder network for medical ultrasound image despeckling
Y Chen, Z Guo, J Yuan, X Li, H Yu - Computers in Biology and Medicine, 2024 - Elsevier
The majority of existing deep learning-based image denoising algorithms mainly focus on
processing the overall image features, ignoring the fine differences between the semantic …
processing the overall image features, ignoring the fine differences between the semantic …
Transformers Fusion across Disjoint Samples for Hyperspectral Image Classification
3D Swin Transformer (3D-ST) known for its hierarchical attention and window-based
processing, excels in capturing intricate spatial relationships within images. Spatial-spectral …
processing, excels in capturing intricate spatial relationships within images. Spatial-spectral …
[HTML][HTML] Applying Swin Architecture to diverse Sign Language Datasets
In an era where artificial intelligence (AI) bridges crucial communication gaps, this study
extends AI's utility to American and Taiwan Sign Language (ASL and TSL) communities …
extends AI's utility to American and Taiwan Sign Language (ASL and TSL) communities …
Batch-Transformer for Scene Text Image Super Resolution
Y Sun, X Xie, Z Li - 2024 - researchsquare.com
Recognizing low-resolution text images is challenging as they often lose their detailed
information, leading to poor recognition accuracy. Moreover, the traditional methods, based …
information, leading to poor recognition accuracy. Moreover, the traditional methods, based …